Spatiotemporal Correlation

Spatiotemporal correlation analysis focuses on understanding and modeling the interconnectedness of data points across both space and time. Current research heavily emphasizes developing sophisticated neural network architectures, such as graph convolutional networks (GCNs) and transformers, often combined with recurrent neural networks (RNNs), to capture these complex relationships in diverse datasets. These advancements are driving improvements in forecasting tasks across various domains, including traffic prediction, weather forecasting, and anomaly detection in multivariate time series, leading to more accurate and efficient predictions. The ability to effectively model spatiotemporal correlations has significant implications for numerous applications requiring accurate predictions based on spatially and temporally distributed data.

Papers

November 2, 2024

An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals
Jiahui Jin, Yi Hong, Guandong Xu, Jinghui Zhang, Jun Tang, Hancheng Wang
Extension Study Spatiotemporal Correlation Time Interval Crime Pattern Object Centric Event Spatio Temporal Point Process

October 25, 2024

Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems
Jindong Tian, Yuxuan Liang, Ronghui Xu, Peng Chen, Chenjuan Guo, Aoying Zhou, Lujia Pan, Zhongwen Rao, Bin Yang
System Description Neural ODE Data Driven Model Physic Based Model Air Quality Forecasting Spatiotemporal Correlation Air Quality Prediction

August 6, 2024

A Differential Smoothness-based Compact-Dynamic Graph Convolutional Network for Spatiotemporal Signal Recovery
Pengcheng Gao, Zicheng Gao, Ye Yuan
Dynamic Graph Spatiotemporal Correlation Spatiotemporal Signal

June 17, 2024

Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework
Jiaqi Lin, Qianqian Ren
Vision Transformer Traffic Prediction Temporal Transformer Spatio Temporal Forecasting Spatiotemporal Correlation Temporal Layer

May 30, 2024

MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
Zhicheng Chen, Xi Xiao, Ke Xu, Zhong Zhang, Yu Rong, Qing Li, Guojun Gan, Zhiqiang Xu, Peilin Zhao
Fine Grained Multivariate Time Series Time Series Prediction Spatiotemporal Correlation Forecasting Algorithm Prediction Network

May 16, 2024

Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation
Guojun Liang, Prayag Tiwari, Slawomir Nowaczyk, Stefan Byttner
Graph Neural Network Spatio Temporal Time Series Imputation Spatiotemporal Correlation

February 14, 2024

Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Huachen Fang, Jinjian Wu, Qibin Hou, Weisheng Dong, Guangming Shi
Denoising Process Spatiotemporal Correlation Color Image Denoising

January 16, 2024

Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network
Beibei Yang, Weiling Li, Yan Fang
Representation Learning Event Camera Tensor Network Spatiotemporal Representation Event Stream Spatiotemporal Correlation Sparse Event

December 4, 2023

EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series
Jie Liu, Qilin Li, Senjian An, Bradley Ezard, Ling Li
Anomaly Detection Transformer Based Multivariate Time Series Dynamic Graph Spatiotemporal Correlation Transformer Based Anomaly Detection

September 16, 2023

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting
Zhiwei Zhang, Weizhong Zhang, Yaowei Huang, Kani Chen
Time Series Traffic Prediction Traffic Forecasting Test Time Training Traffic Video Spatiotemporal Correlation

September 13, 2023

Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel
Lorenzo Booth, Stefano Carpin
Gaussian Process RSD Difference of Gaussian Informative Path Planning Spatiotemporal Correlation Unmanned Vehicle Dynamic Reconstruction

June 12, 2023

Dynamic Causal Graph Convolutional Network for Traffic Prediction
Junpeng Lin, Ziyue Li, Zhishuai Li, Lei Bai, Rui Zhao, Chen Zhang
Temporal Dependency Traffic Prediction Traffic Datasets Spatiotemporal Correlation Traffic Congestion Propagation

April 4, 2023

Effects of spatiotemporal correlations in wind data on neural network-based wind predictions
Heesoo Shin, Mario Rüttgers, Sangseung Lee
Mixed Effect Spatiotemporal Correlation Wind Data

March 14, 2023

FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting
Junhao Zhang, Junjie Tang, Juncheng Jin, Zehui Qu
Traffic Data Traffic Flow Forecasting Spatiotemporal Correlation Pure Transformer

January 27, 2023

Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective
Xu Wang, Pengfei Gu, Pengkun Wang, Binwu Wang, Zhengyang Zhou, Lei Bai, Yang Wang
Raw Data Graph Learning Temporal Graph Learning Free Learning Spatiotemporal Correlation Spatiotemporal Learning

November 7, 2022

Spatiotemporal forecasting of vertical track alignment with exogenous factors
Katsuya Kosukegawa, Yasukuni Mori, Hiroki Suyari, Kazuhiko Kawamoto
Spatiotemporal Forecasting Spatial Correlation Spatiotemporal Correlation Exogenous Variable Track Geometry

September 29, 2022

A case study of spatiotemporal forecasting techniques for weather forecasting
Shakir Showkat Sofi, Ivan Oseledets
Case Study Numerical Weather Prediction Weather Forecasting Climate Data Spatiotemporal Forecasting Spatiotemporal Prediction Spatiotemporal Correlation

September 8, 2022

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
Naghmeh Shafiee Roudbari, Zachary Patterson, Ursula Eicker, Charalambos Poullis
Graph Neural Network Recurrent Neural Network Traffic Forecasting Spatio Temporal Forecasting Spatiotemporal Correlation Multilevel Abstraction

August 11, 2022

Uncertainty Quantification for Traffic Forecasting: A Unified Approach
Weizhu Qian, Dalin Zhang, Yan Zhao, Kai Zheng, James J. Q. Yu
Uncertainty Quantification Unified Framework Traffic Forecasting Traffic Data Spatiotemporal Correlation Deep Uncertainty

June 18, 2022

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen
Traffic Forecasting Temporal Graph Neural Network Temporal Correlation Traffic Network Spatial Temporal Graph Spatiotemporal Correlation

Spatiotemporal Correlation

Papers

An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals

Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems

A Differential Smoothness-based Compact-Dynamic Graph Convolutional Network for Spatiotemporal Signal Recovery

Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework

MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series

Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation

Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement

Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network

EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting

Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel

Dynamic Causal Graph Convolutional Network for Traffic Prediction

Effects of spatiotemporal correlations in wind data on neural network-based wind predictions

FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting

Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective

Spatiotemporal forecasting of vertical track alignment with exogenous factors

A case study of spatiotemporal forecasting techniques for weather forecasting

Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction

Uncertainty Quantification for Traffic Forecasting: A Unified Approach

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting